Baker, B.D., & Richards, C.E. (2004). The ecology of educational systems: Data models and tools for improvisational leading and learning. Upper Saddle River, NJ: Pearson Merrill Prentice-Hall.
Other readings as assigned
Coursework:
Decision Analysis I is a course that focuses on quantitative decision making in organizations, specifically educational organizations. The class concentrates on practical applications of different analytical data tools and techniques, which are grounded in theory and sound methodological research to examine authentic organizational contexts and drive decision-making. Decision Analysis I emphasizes the use of microcomputers for quantitative decision-making.
9.8: Course introduction; Pre-Assessment; Theory and Research; Databases; Finding and Presenting Data I- In-Class Task
Course Pre-Assessment: Why is quantitative analysis important to educational or organizational decision-making? What data tools can be utilized to promote sound analytical
expertise to support decision-making in organizations?
[HW - Read Chapters 2 & 3 (Baker & Richards)]
9.13 Finding and Presenting Data II; Writing Policy Briefs and Memos; Organizing Data; Cleaning Data; Data Management; Filtering; Sorting; Manipulation - In-Class Task
[HW – Complete In-Class Task]
9.20 Descriptive Statistics; Mining Data; Presenting Descriptive Statistics [Writing and Visual] – In-Class Task; Distribute and Begin Assignment #1 (if time permits)
[HW - Read Chapter 4 (Baker & Richards)]
9.27 Indicators and Ratios; Ratios; Value-Added Models (VAM); Building Simple and Advanced Indexes; Distribute and Begin Assignment #1
10.4 Standard Distributions; Understanding a Descriptive Statistics Array; Ranks; Percentiles; Comparing Distributions and Statistical Significance; Similarities and Differences; Group Analysis; Educational Research and Practice- what to believe? In-Class Task; Work on Assignment #1 if time permits
[HW – Read Chapter 6 & 7 (Baker & Richards)]
10.11 Time as a Variable; Organizational Relationships; x and y values; Scatter plots; Simple Correlation and Significance; Effect Sizes; Lines of Best Fit; Input-Outcome Relationships;
In-Class Task; Distribute and Assignment #1 Due
[HW – Read Chapters 8 & 9 (Baker & Richards)]
10.18 Building databases; Manipulation of data with relationships, indexes, and other tools; In-Class Task
[HW – Work on In-Class Task]
10.25 Quasi-Action Research; Data Work Session with In-Class Task; Distribute Assignment #2
11.1 Explore Data Tools for Advanced Manipulation and Statistical Modeling; In-Class Task
[HW - Complete In-Class Task; Assignment #2]
11.8 Work Session
[HW – Complete Assignment #2]
11.15 Distribute Demo Task; Demonstration Task Review; Organizational and School Level Data-Driven Decision-Making; Technical Manuals and Getting to the DATA; Case Study: Litigation, Religion and Politics (Galinski, in Hoy & Miskel, 2001); Case Study: Scandal at Placido High: Coincidence or Conspiracy (DiPaola, in Hoy & Miskel, 2001).
[HW – Demonstration Task]
11.29 Classroom Level Data-Driven Decision-Making; Evaluation and Communication
[HW – Complete In-Class Task; Demonstration Task]
12.6 Work Session - Peer Review Session – Prep for Final Demonstration Task
[HW – Demonstration Task]
12.13 Demonstration Tasks Due; Presentations
[HW – Demonstration Task]
12.20 Course Closeout; Linking Theory and Research to Practice;Course Review and Analysis Required Upload to SAKAI
NOTE: There will be additional readings assigned as the course progresses.
Course Requirements:
Students will be expected to: a) Participate in class discussions, synthesize information, and provide insightful commentary based on readings, lectures, and practical experiences, b) submit all classroom tasks on-time, c) work individually and/or in small groups to complete class tasks and assignments that serve as evidence of functional understanding and proficient use of data in organizational decision-making, d) deliver presentations as assigned, and e) complete all readings and written assignments on time. There is not a mechanism available for handing in assignments late. Late assignments penalized.
Knowledge Objectives: TLWBT – - Identify multiple modes of quantitative data and synthesize data using multiple tools for analysis (Indicators, Ratios, Descriptive Statistics, Finding and Interpreting Relationships, Evaluating Change over Time, etc.) - Evaluate current issues in educational organizations using learned data analysis tools - Analyze, evaluate and find relationships between multiple data that yield evidence to the contrary of what is considered normal or practical - Find and interpret statistical understanding of data in authentic contexts - Use Data to Drive Decision-Making - Synthesize the principles of research-based data-analysis to guide focused decision making - Exhibit leadership and organizational skills while working in a cooperative group situation
Assignment #1 20 points possible Assignment #2 20 Points Possible Assignment #3 -Demonstration Task 20 Points Possible -Presentation (Group) 10 Points Possible Class/Take-Home Assignments 15 Points Possible Participation/Attendance/Willingness 15 points possible
to embrace new positions when presented
with new knowledge/Synthesis of readings/Depth of answers.
(poor attendance will negatively affect final grade)
(minimum of 2pts off final grade for each class missed)
(late papers penalized ½ grade: A to B+, etc.)
Recommended Readings
Bloom, B. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-on-one tutoring. Educational Researcher, 13(6), 4-16.
Choi, K., Goldschmidt, P., & Yamashiro, K. (2005) Exploring models of school performance: From theory to practice. In J.L. Herman & E.H. Haertel (Eds.), Uses and misuses of data for educational accountability and improvement: The 104thyearbook of the National Society for the Study of Education (pp. 119-146). Malden, MA: Blackwell Publishing.
Coleman, James S. (1966). Equality of educational opportunity study [Computer file]. ICPSR06389-v3. Washington, DC: U.S. Department of Health, Education, and Welfare, Office of Education/National Center for Education Statistics [producer], 1999. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2007-04-27. doi:10.3886/ICPSR06389
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Goodlad, J. (1984). A place called school: Prospects for the future. New York: McGraw-Hill.
Smith, E.R., & Tyler, R.W. (1942). Appraising and reporting student progress. New York, NY: Harper and Row.
Tramaglini, T.W. (2007). Dangers of percentages proficient: Analysis of interpretations of high-stakes assessment results on the New Jersey School Report Card. New Jersey Journal of Supervision and Curriculum Development, 52(1), 18-32.
Wang, M.C., Haertel, G.D., & Walberg, H.J. (1993). Toward a knowledge base for school learning. Review of Educational Research, 63(3), 249-294.
Zhao, Yong. (2009). Catching up or leading the way: American education in the age of globalization. Alexandria, VA: Association for Supervision and Curriculum Development.
Graduate School of Education
Decision Analysis I – 15:230:522
Fall 2010: Monday 7:40 PM – 10:20 PM - ED208
Instructor: Dr. Thomas W. Tramaglini
(732) 761-2135 (Office)
(732) 713-4899 (Cell)
T2Education@gmail.com
ttram@freeholdboro.k12.nj.us
Required Texts:
Baker, B.D., & Richards, C.E. (2004). The ecology of educational systems: Data models and tools for improvisational leading and learning. Upper Saddle River, NJ: Pearson Merrill Prentice-Hall.
Other readings as assigned
Coursework:
Decision Analysis I is a course that focuses on quantitative decision making in organizations, specifically educational organizations. The class concentrates on practical applications of different analytical data tools and techniques, which are grounded in theory and sound methodological research to examine authentic organizational contexts and drive decision-making. Decision Analysis I emphasizes the use of microcomputers for quantitative decision-making.
9.8: Course introduction; Pre-Assessment; Theory and Research; Databases; Finding and Presenting Data I- In-Class Task
Course Pre-Assessment: Why is quantitative analysis important to educational or organizational decision-making? What data tools can be utilized to promote sound analytical
expertise to support decision-making in organizations?
[HW - Read Chapters 2 & 3 (Baker & Richards)]
9.13 Finding and Presenting Data II; Writing Policy Briefs and Memos; Organizing Data; Cleaning Data; Data Management; Filtering; Sorting; Manipulation - In-Class Task
[HW – Complete In-Class Task]
9.20 Descriptive Statistics; Mining Data; Presenting Descriptive Statistics [Writing and Visual] – In-Class Task; Distribute and Begin Assignment #1 (if time permits)
[HW - Read Chapter 4 (Baker & Richards)]
9.27 Indicators and Ratios; Ratios; Value-Added Models (VAM); Building Simple and Advanced Indexes; Distribute and Begin Assignment #1
[HW – Read Chapter 5 (Baker & Richards); Assignment #1]
10.4 Standard Distributions; Understanding a Descriptive Statistics Array; Ranks; Percentiles; Comparing Distributions and Statistical Significance; Similarities and Differences; Group Analysis; Educational Research and Practice- what to believe? In-Class Task; Work on Assignment #1 if time permits
[HW – Read Chapter 6 & 7 (Baker & Richards)]
10.11 Time as a Variable; Organizational Relationships; x and y values; Scatter plots; Simple Correlation and Significance; Effect Sizes; Lines of Best Fit; Input-Outcome Relationships;
In-Class Task; Distribute and Assignment #1 Due
[HW – Read Chapters 8 & 9 (Baker & Richards)]
10.18 Building databases; Manipulation of data with relationships, indexes, and other tools; In-Class Task
[HW – Work on In-Class Task]
10.25 Quasi-Action Research; Data Work Session with In-Class Task; Distribute Assignment #2
[HW – Read Chapters 10 (Baker & Richards); Assignment #2]
11.1 Explore Data Tools for Advanced Manipulation and Statistical Modeling; In-Class Task
[HW - Complete In-Class Task; Assignment #2]
11.8 Work Session
[HW – Complete Assignment #2]
11.15 Distribute Demo Task; Demonstration Task Review; Organizational and School Level Data-Driven Decision-Making; Technical Manuals and Getting to the DATA; Case Study: Litigation, Religion and Politics (Galinski, in Hoy & Miskel, 2001); Case Study: Scandal at Placido High: Coincidence or Conspiracy (DiPaola, in Hoy & Miskel, 2001).
[HW – Demonstration Task]
11.29 Classroom Level Data-Driven Decision-Making; Evaluation and Communication
[HW – Complete In-Class Task; Demonstration Task]
12.6 Work Session - Peer Review Session – Prep for Final Demonstration Task
[HW – Demonstration Task]
12.13 Demonstration Tasks Due; Presentations
[HW – Demonstration Task]
12.20 Course Closeout; Linking Theory and Research to Practice;Course Review and Analysis Required Upload to SAKAI
NOTE: There will be additional readings assigned as the course progresses.
Course Requirements:
Students will be expected to: a) Participate in class discussions, synthesize information, and provide insightful commentary based on readings, lectures, and practical experiences, b) submit all classroom tasks on-time, c) work individually and/or in small groups to complete class tasks and assignments that serve as evidence of functional understanding and proficient use of data in organizational decision-making, d) deliver presentations as assigned, and e) complete all readings and written assignments on time. There is not a mechanism available for handing in assignments late. Late assignments penalized.
Knowledge Objectives: TLWBT –
- Identify multiple modes of quantitative data and synthesize data using multiple tools for analysis (Indicators, Ratios, Descriptive Statistics, Finding and Interpreting Relationships, Evaluating Change over Time, etc.)
- Evaluate current issues in educational organizations using learned data analysis tools
- Analyze, evaluate and find relationships between multiple data that yield evidence to the contrary of what is considered normal or practical
- Find and interpret statistical understanding of data in authentic contexts
- Use Data to Drive Decision-Making
- Synthesize the principles of research-based data-analysis to guide focused decision making
- Exhibit leadership and organizational skills while working in a cooperative group situation
NJPSTSL Standards Assessed: (1.2-5; 1.11, 1.13) (2.1, 6,9-10) (3.2, 5, 9-10) (4.1) (5.9) (6.5, 11, 14-15, 19)
Grading:
Assignment #1 20 points possible
Assignment #2 20 Points Possible
Assignment #3
-Demonstration Task 20 Points Possible
-Presentation (Group) 10 Points Possible
Class/Take-Home Assignments 15 Points Possible
Participation/Attendance/Willingness 15 points possible
to embrace new positions when presented
with new knowledge/Synthesis of readings/Depth of answers.
(poor attendance will negatively affect final grade)
(minimum of 2pts off final grade for each class missed)
(late papers penalized ½ grade: A to B+, etc.)
Recommended Readings
Bloom, B. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-on-one tutoring. Educational Researcher, 13(6), 4-16.
Choi, K., Goldschmidt, P., & Yamashiro, K. (2005) Exploring models of school performance: From theory to practice. In J.L. Herman & E.H. Haertel (Eds.), Uses and misuses of data for educational accountability and improvement: The 104th yearbook of the National Society for the Study of Education (pp. 119-146). Malden, MA: Blackwell Publishing.
Coleman, James S. (1966). Equality of educational opportunity study [Computer file]. ICPSR06389-v3. Washington, DC: U.S. Department of Health, Education, and Welfare, Office of Education/National Center for Education Statistics [producer], 1999. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2007-04-27. doi:10.3886/ICPSR06389
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Goodlad, J. (1984). A place called school: Prospects for the future. New York: McGraw-Hill.
Smith, E.R., & Tyler, R.W. (1942). Appraising and reporting student progress. New York, NY: Harper and Row.
Tramaglini, T.W. (2007). Dangers of percentages proficient: Analysis of interpretations of high-stakes assessment results on the New Jersey School Report Card. New Jersey Journal of Supervision and Curriculum Development, 52(1), 18-32.
Wang, M.C., Haertel, G.D., & Walberg, H.J. (1993). Toward a knowledge base for school learning. Review of Educational Research, 63(3), 249-294.
Zhao, Yong. (2009). Catching up or leading the way: American education in the age of globalization. Alexandria, VA: Association for Supervision and Curriculum Development.